Identification Of Disease Genes Research Articles

GPS: Identification of disease genes by rank aggregation of multi-genomic scoring schemes

In solving the gene prioritization problem, ranking candidate genes from most to least promising is attempted before further experimental validation. Integrating the results of various data sources and methods tends to result in a better performance when solving the gene prioritization problem. Therefore, a wide range of datasets and algorithms was investigated; these included topological features of protein networks, physicochemical characteristics and blast similarity scores of protein sequences, gene ontology, biological pathways, and tissue-based data sources. The novelty of this study lies in how the best-performing methods and reliable multi-genomic data sources were applied in an efficient two-step approach. In the first step, various multi-genomic data sources and algorithms were evaluated and seven best-performing rankers were then applied to prioritize candidate genes in different ways. In the second step, global prioritization was obtained by aggregating several scoring schemes.The results showed that protein networks, functional linkage networks, gene ontology, and biological pathway data sources have a significant impact on the quality of the gene prioritization approach. The findings also demonstrated a direct relationship between the degree of genes and the ranking quality of the evaluated tools. This approach outperformed previously published algorithms (e.g., DIR, GPEC, GeneDistiller, and Endeavour) in all evaluation metrices and led to the development of GPS software. Its user-friendly interface and accuracy makes GPS a powerful tool for the identification of human disease genes. GPS is available at http://gpsranker.com and http://LBB.ut.ac.ir.

Hereditary palmoplantar keratodermas. Part I. Non-syndromic palmoplantar keratodermas: classification, clinical and genetic features.

The term palmoplantar keratoderma (PPK) indicates any form of persistent thickening of the epidermis of palms and soles and includes genetic as well as acquired conditions. We review the nosology of hereditary PPKs that comprise an increasing number of entities with different prognoses, and a multitude of associated cutaneous and extracutaneous features. On the basis of the phenotypic consequences of the underlying genetic defect, hereditary PPKs may be divided into the following: (i) non-syndromic, isolated PPKs, which are characterized by a unique or predominant palmoplantar involvement; (ii) non-syndromic PPKs with additional distinctive cutaneous and adnexal manifestations, here named complex PPKs; (iii) syndromic PPKs, in which PPK is associated with specific extracutaneous manifestations. To date, the diagnosis of the different hereditary PPKs is based mainly on clinical history and features combined with histopathological findings. In recent years, the exponentially increasing use of next-generation sequencing technologies has led to the identification of several novel disease genes, and thus substantially contributed to elucidate the molecular basis of such a heterogeneous group of disorders. Here, we focus on hereditary non-syndromic isolated and complex PPKs. Syndromic PPKs are reviewed in the second part of this 2-part article, where other well-defined genetic diseases, which may present PPK among their phenotypic manifestations, are also listed and diagnostic and therapeutic approaches for PPKs are summarized.

PBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion.

MotivationComputational gene prioritization can aid in disease gene identification. Here, we propose pBRIT (prioritization using Bayesian Ridge regression and Information Theoretic model), a novel adaptive and scalable prioritization tool, integrating Pubmed abstracts, Gene Ontology, Sequence similarities, Mammalian and Human Phenotype Ontology, Pathway, Interactions, Disease Ontology, Gene Association database and Human Genome Epidemiology database, into the prediction model. We explore and address effects of sparsity and inter-feature dependencies within annotation sources, and the impact of bias towards specific annotations.ResultspBRIT models feature dependencies and sparsity by an Information-Theoretic (data driven) approach and applies intermediate integration based data fusion. Following the hypothesis that genes underlying similar diseases will share functional and phenotype characteristics, it incorporates Bayesian Ridge regression to learn a linear mapping between functional and phenotype annotations. Genes are prioritized on phenotypic concordance to the training genes. We evaluated pBRIT against nine existing methods, and on over 2000 HPO-gene associations retrieved after construction of pBRIT data sources. We achieve maximum AUC scores ranging from 0.92 to 0.96 against benchmark datasets and of 0.80 against the time-stamped HPO entries, indicating good performance with high sensitivity and specificity. Our model shows stable performance with regard to changes in the underlying annotation data, is fast and scalable for implementation in routine pipelines.Availability and implementation http://biomina.be/apps/pbrit/; https://bitbucket.org/medgenua/pbrit.Supplementary information Supplementary data are available at Bioinformatics online.

Despite Challenges and Pitfalls: How Ophthalmology Benefits from the Use of Next-Generation Sequencing

Within a few years, high-throughput sequencing (next-generation sequencing, NGS) has become a routine method in genetic diagnostics and has largely replaced conventional Sanger sequencing. The complexity of NGS data requires sound bioinformatic analysis: pinpointing the disease-causing variants may be difficult, and erroneous interpretations must be avoided. When looking at the group of retinal dystrophies as an example of eye disorders with extensive genetic heterogeneity, one can clearly say that NGS-based diagnostics yield important information for most patients and physicians, and that it has furthered our knowledge significantly. Furthermore, NGS has accelerated ophthalmogenetic research aimed at the identification of novel eye disease genes.

Induced Pluripotent Stem Cells in Disease Modeling and Gene Identification.

Experimental modeling of human inherited disorders provides insight into the cellular and molecular mechanisms involved, and the underlying genetic component influencing, the disease phenotype. The breakthrough development of induced pluripotent stem cell (iPSC) technology represents a quantum leap in experimental modeling of human diseases, providing investigators with a self-renewing and, thus, unlimited source of pluripotent cells for targeted differentiation. In principle, the entire range of cell types found in the human body can be interrogated using an iPSC approach. Therefore, iPSC technology, and the increasingly refined abilities to differentiate iPSCs into disease-relevant target cells, has far-reaching implications for understanding disease pathophysiology, identifying disease-causing genes, and developing more precise therapeutics, including advances in regenerative medicine. In this chapter, we discuss the technological perspectives and recent developments in the application of patient-derived iPSC lines for human disease modeling and disease gene identification.

The Reconstruction and Analysis of Gene Regulatory Networks.

In post-genomic era, an important task is to explore the function of individual biological molecules (i.e., gene, noncoding RNA, protein, metabolite) and their organization in living cells. For this end, gene regulatory networks (GRNs) are constructed to show relationship between biological molecules, in which the vertices of network denote biological molecules and the edges of network present connection between nodes (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). Biologists can understand not only the function of biological molecules but also the organization of components of living cells through interpreting the GRNs, since a gene regulatory network is a comprehensively physiological map of living cells and reflects influence of genetic and epigenetic factors (Strogatz, Nature 410:268-276, 2001; Bray, Science 301:1864-1865, 2003). In this paper, we will review the inference methods of GRN reconstruction and analysis approaches of network structure. As a powerful tool for studying complex diseases and biological processes, the applications of the network method in pathway analysis and disease gene identification will be introduced.

What Can We Learn About Human Disease from the Nematode C. elegans?

Numerous approaches have been taken in the hunt for human disease genes. The identification of such genes not only provides a great deal of information about the mechanism of disease development, but also provides potential avenues for better diagnosis and treatment. In this chapter, we review the use of the nonmammalian model organism C. elegans for the identification of human disease genes. Studies utilizing this relatively simple organism offer a good balance between the ability to recapitulate many aspects of human disease, while still offering an abundance of powerful cell biological, genetic, and genomic tools for disease gene discovery. C. elegans and other nonmammalian models have produced, and will continue to produce, key insights into human disease pathogenesis.

PGA: post-GWAS analysis for disease gene identification.

Although the genome-wide association study (GWAS) is a powerful method to identify disease-associated variants, it does not directly address the biological mechanisms underlying such genetic association signals. Here, we present PGA, a Perl- and Java-based program for post-GWAS analysis that predicts likely disease genes given a list of GWAS-reported variants. Designed with a command line interface, PGA incorporates genomic and eQTL data in identifying disease gene candidates and uses gene network and ontology data to score them based upon the strength of their relationship to the disease in question. http://zdzlab.einstein.yu.edu/1/pga.html. zhengdong.zhang@einstein.yu.edu. Supplementary data are available at Bioinformatics online.

Pakistan Genetic Mutation Database (PGMD); A centralized Pakistani mutome data source

The development and advancement of next generation sequencing have not only sped up the process of identifying rare variants, but have also enabled scientists to explore all variants in a single individual. The Pakistani population has a high ratio of first degree consanguinity, which is why it is a rich source for various kinds of genetic disorders.Due to the heterogeneous composition of Pakistani population, the likelihood of genetic heterogeneity for each disorder is high. Therefore, the compilation and organization of such vast genetic data is necessary to facilitate access for analysis and interpretation to researchers and medical geneticists.The increased research on Pakistani ethnic families for disease gene identification has revealed many mutations, which has led us to develop a Pakistani mutome database entitled “Pakistan Genetic Mutation Database (PGMD)”. In PGMD, the medico-genetic information about diseases are mainly compiled into Syndromic and Non-syndromic disorders. It is a public database, which can be freely accessed from http://www.pakmutation.com.At present, we have registered more than 1000 mutations, reported in about 130 different kinds of genetic disorders. Practically, PGMD will assist researchers, clinicians, and geneticists in genetic counseling and screening of population-specific mutations, which will also aid in personalized healthcare.

Exome Pool-Seq in neurodevelopmental disorders

High throughput sequencing has greatly advanced disease gene identification, especially in heterogeneous entities. Despite falling costs this is still an expensive and laborious technique, particularly when studying large cohorts. To address this problem we applied Exome Pool-Seq as an economic and fast screening technology in neurodevelopmental disorders (NDDs). Sequencing of 96 individuals can be performed in eight pools of 12 samples on less than one Illumina sequencer lane. In a pilot study with 96 cases we identified 27 variants, likely or possibly affecting function. Twenty five of these were identified in 923 established NDD genes (based on SysID database, status November 2016) (ACTB, AHDC1, ANKRD11, ATP6V1B2, ATRX, CASK, CHD8, GNAS, IFIH1, KCNQ2, KMT2A, KRAS, MAOA, MED12, MED13L, RIT1, SETD5, SIN3A, TCF4, TRAPPC11, TUBA1A, WAC, ZBTB18, ZMYND11), two in 543 (SysID) candidate genes (ZNF292, BPTF), and additionally a de novo loss-of-function variant in LRRC7, not previously implicated in NDDs. Most of them were confirmed to be de novo, but we also identified X-linked or autosomal-dominantly or autosomal-recessively inherited variants. With a detection rate of 28%, Exome Pool-Seq achieves comparable results to individual exome analyses but reduces costs by >85%. Compared with other large scale approaches using Molecular Inversion Probes (MIP) or gene panels, it allows flexible re-analysis of data. Exome Pool-Seq is thus well suited for large-scale, cost-efficient and flexible screening in characterized but heterogeneous entities like NDDs.

High-throughput Analysis of Locomotor Behavior in the Drosophila Island Assay.

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other neurological diseases at an increasing speed. However, little is known about the molecular mechanisms that underlie these disorders. The genetic, molecular, and behavioral toolbox of Drosophila melanogaster makes this model organism particularly useful to characterize new disease genes and mechanisms in a high-throughput manner. Nevertheless, high-throughput screens require efficient and reliable assays that, ideally, are cost-effective and allow for the automatized quantification of traits relevant to these disorders. The island assay is a cost-effective and easily set-up method to evaluate Drosophila locomotor behavior. In this assay, flies are thrown onto a platform from a fixed height. This induces an innate motor response that enables the flies to escape from the platform within seconds. At present, quantitative analyses of filmed island assays are done manually, which is a laborious undertaking, particularly when performing large screens.This manuscript describes the "Drosophila Island Assay" and "Island Assay Analysis" algorithms for high-throughput, automated data processing and quantification of island assay data. In the setup, a simple webcam connected to a laptop collects an image series of the platform while the assay is performed. The "Drosophila Island Assay" algorithm developed for the open-source software Fiji processes these image series and quantifies, for each experimental condition, the number of flies on the platform over time. The "Island Assay Analysis" script, compatible with the free software R, was developed to automatically process the obtained data and to calculate whether treatments/genotypes are statistically different. This greatly improves the efficiency of the island assay and makes it a powerful readout for basic locomotion and flight behavior. It can thus be applied to large screens investigating fly locomotor ability, Drosophila models of movement disorders, and drug efficacy.

High-throughput Analysis of Locomotor Behavior in the <em>Drosophila </em>Island Assay

Advances in next-generation sequencing technologies contribute to the identification of (candidate) disease genes for movement disorders and other neurological diseases at an increasing speed. However, little is known about the molecular mechanisms that underlie these disorders. The genetic, molecular, and behavioral toolbox of Drosophila melanogaster makes this model organism particularly useful to characterize new disease genes and mechanisms in a high-throughput manner. Nevertheless, high-throughput screens require efficient and reliable assays that, ideally, are cost-effective and allow for the automatized quantification of traits relevant to these disorders. The island assay is a cost-effective and easily set-up method to evaluate Drosophila locomotor behavior. In this assay, flies are thrown onto a platform from a fixed height. This induces an innate motor response that enables the flies to escape from the platform within seconds. At present, quantitative analyses of filmed island assays are done manually, which is a laborious undertaking, particularly when performing large screens. This manuscript describes the "Drosophila Island Assay" and "Island Assay Analysis" algorithms for high-throughput, automated data processing and quantification of island assay data. In the setup, a simple webcam connected to a laptop collects an image series of the platform while the assay is performed. The "Drosophila Island Assay" algorithm developed for the open-source software Fiji processes these image series and quantifies, for each experimental condition, the number of flies on the platform over time. The "Island Assay Analysis" script, compatible with the free software R, was developed to automatically process the obtained data and to calculate whether treatments/genotypes are statistically different. This greatly improves the efficiency of the island assay and makes it a powerful readout for basic locomotion and flight behavior. It can thus be applied to large screens investigating fly locomotor ability, Drosophila models of movement disorders, and drug efficacy.

Exome sequencing and network analysis identifies shared mechanisms underlying spinocerebellar ataxia.

The autosomal dominant cerebellar ataxias, referred to as spinocerebellar ataxias in genetic nomenclature, are a rare group of progressive neurodegenerative disorders characterized by loss of balance and coordination. Despite the identification of numerous disease genes, a substantial number of cases still remain without a genetic diagnosis. Here, we report five novel spinocerebellar ataxia genes, FAT2, PLD3, KIF26B, EP300, and FAT1, identified through a combination of exome sequencing in genetically undiagnosed families and targeted resequencing of exome candidates in a cohort of singletons. We validated almost all genes genetically, assessed damaging effects of the gene variants in cell models and further consolidated a role for several of these genes in the aetiology of spinocerebellar ataxia through network analysis. Our work links spinocerebellar ataxia to alterations in synaptic transmission and transcription regulation, and identifies these as the main shared mechanisms underlying the genetically diverse spinocerebellar ataxia types.

Chapter 22 - Genetics of neurodegenerative diseases: an overview

Genetic factors are central to the etiology of neurodegeneration, both as monogenic causes of heritable disease and as modifiers of susceptibility to complex, sporadic disorders. Over the last two decades, the identification of disease genes and risk loci has led to some of the greatest advances in medicine and invaluable insights into pathogenic mechanisms and disease pathways. Large-scale research efforts, novel study designs, and advances in methodology are rapidly expanding our understanding of the genome and the genetic architecture of neurodegenerative disease. Here, we review major developments in the field to date, highlighting overarching historic trends and general insights. Monogenic neurodegenerative diseases are discussed from the perspectives of both rare Mendelian forms of common disorders, such as Alzheimer disease and Parkinson disease, and heterogeneous heritable conditions, including ataxias and spastic paraplegias. Next, we summarize the experiences from investigations of complex neurodegenerative disorders, including genomewide association studies. In the final section, we reflect upon the limitations of current findings and outline important future directions. Genetics plays an essential role in translational research, ultimately aiming to develop novel disease-modifying therapies for neurodegenerative disorders. We anticipate that individual genetic profiling will also be increasingly relevant in a clinical context, with implications for patient care in line with the proposed ideal of personalized medicine.

Identification of novel candidate disease genes from de novo exonic copy number variants

BackgroundExon-targeted microarrays can detect small (<1000 bp) intragenic copy number variants (CNVs), including those that affect only a single exon. This genome-wide high-sensitivity approach increases the molecular diagnosis for conditions with known disease-associated genes, enables better genotype–phenotype correlations, and facilitates variant allele detection allowing novel disease gene discovery.MethodsWe retrospectively analyzed data from 63,127 patients referred for clinical chromosomal microarray analysis (CMA) at Baylor Genetics laboratories, including 46,755 individuals tested using exon-targeted arrays, from 2007 to 2017. Small CNVs harboring a single gene or two to five non-disease-associated genes were identified; the genes involved were evaluated for a potential disease association.ResultsIn this clinical population, among rare CNVs involving any single gene reported in 7200 patients (11%), we identified 145 de novo autosomal CNVs (117 losses and 28 intragenic gains), 257 X-linked deletion CNVs in males, and 1049 inherited autosomal CNVs (878 losses and 171 intragenic gains); 111 known disease genes were potentially disrupted by de novo autosomal or X-linked (in males) single-gene CNVs. Ninety-one genes, either recently proposed as candidate disease genes or not yet associated with diseases, were disrupted by 147 single-gene CNVs, including 37 de novo deletions and ten de novo intragenic duplications on autosomes and 100 X-linked CNVs in males. Clinical features in individuals with de novo or X-linked CNVs encompassing at most five genes (224 bp to 1.6 Mb in size) were compared to those in individuals with larger-sized deletions (up to 5 Mb in size) in the internal CMA database or loss-of-function single nucleotide variants (SNVs) detected by clinical or research whole-exome sequencing (WES). This enabled the identification of recently published genes (BPTF, NONO, PSMD12, TANGO2, and TRIP12), novel candidate disease genes (ARGLU1 and STK3), and further confirmation of disease association for two recently proposed disease genes (MEIS2 and PTCHD1). Notably, exon-targeted CMA detected several pathogenic single-exon CNVs missed by clinical WES analyses.ConclusionsTogether, these data document the efficacy of exon-targeted CMA for detection of genic and exonic CNVs, complementing and extending WES in clinical diagnostics, and the potential for discovery of novel disease genes by genome-wide assay.

Constructing an integrated gene similarity network for the identification of disease genes

BackgroundDiscovering novel genes that are involved human diseases is a challenging task in biomedical research. In recent years, several computational approaches have been proposed to prioritize candidate disease genes. Most of these methods are mainly based on protein-protein interaction (PPI) networks. However, since these PPI networks contain false positives and only cover less half of known human genes, their reliability and coverage are very low. Therefore, it is highly necessary to fuse multiple genomic data to construct a credible gene similarity network and then infer disease genes on the whole genomic scale.ResultsWe proposed a novel method, named RWRB, to infer causal genes of interested diseases. First, we construct five individual gene (protein) similarity networks based on multiple genomic data of human genes. Then, an integrated gene similarity network (IGSN) is reconstructed based on similarity network fusion (SNF) method. Finally, we employee the random walk with restart algorithm on the phenotype-gene bilayer network, which combines phenotype similarity network, IGSN as well as phenotype-gene association network, to prioritize candidate disease genes. We investigate the effectiveness of RWRB through leave-one-out cross-validation methods in inferring phenotype-gene relationships. Results show that RWRB is more accurate than state-of-the-art methods on most evaluation metrics. Further analysis shows that the success of RWRB is benefited from IGSN which has a wider coverage and higher reliability comparing with current PPI networks. Moreover, we conduct a comprehensive case study for Alzheimer’s disease and predict some novel disease genes that supported by literature.ConclusionsRWRB is an effective and reliable algorithm in prioritizing candidate disease genes on the genomic scale. Software and supplementary information are available at http://nclab.hit.edu.cn/~tianzhen/RWRB/.

Phenotype analysis of congenital and neurodevelopmental disorders in the next generation sequencing era.

The designation, phenotype, was proposed as a term by Wilhelm Johannsen in 1909. The word is derived from the Greek, phano (showing) and typo (type), phanotypos. Phenotype has become a widely recognized term, even outside of the genetics community, in recent years with the ongoing identification of human disease genes. The term has been defined as the observable constitution of an organism, but sometimes refers to a condition when a person has a particular clinical presentation. Analysis of phenotype is a timely theme because advances in the understanding of the genetic basis of human disease and the emergence of next generation sequencing have spurred a renewed interest in phenotype and the proposal to establish a "Human Phenome Project." This article summarizes the principles of phenotype analysis that are important in medical genetics and describes approaches to comprehensive phenotype analysis in the investigation of patients with human disorders. I discuss the various elements related to disease phenotypes and highlight neurofibromatosis type 1 and the Elements of Morphology Project as illustrations of the principles. In recent years, the notion of "deep phenotyping" has emerged. Currently there are now a number of proposed strategies and resources to approach this concept. Not since the 1960s and 1970s has there been such an exciting time in the history of medicine surrounding the analysis of phenotype in genetic disorders.

Brain-coX: investigating and visualising gene co-expression in seven human brain transcriptomic datasets

BackgroundThe pathogenesis of neurological and mental health disorders often involves multiple genes, complex interactions, as well as brain- and development-specific biological mechanisms. These characteristics make identification of disease genes for such disorders challenging, as conventional prioritisation tools are not specifically tailored to deal with the complexity of the human brain. Thus, we developed a novel web-application—brain-coX—that offers gene prioritisation with accompanying visualisations based on seven gene expression datasets in the post-mortem human brain, the largest such resource ever assembled.ResultsWe tested whether our tool can correctly prioritise known genes from 37 brain-specific KEGG pathways and 17 psychiatric conditions. We achieved average sensitivity of nearly 50%, at the same time reaching a specificity of approximately 75%. We also compared brain-coX’s performance to that of its main competitors, Endeavour and ToppGene, focusing on the ability to discover novel associations. Using a subset of the curated SFARI autism gene collection we show that brain-coX’s prioritisations are most similar to SFARI’s own curated gene classifications.Conclusionsbrain-coX is the first prioritisation and visualisation web-tool targeted to the human brain and can be freely accessed via http://shiny.bioinf.wehi.edu.au/freytag.s/.

The Missing LINC for Genetic Cardiovascular Disease?

As of 2014, cardiovascular disease (CVD) was listed as the underlying cause for 1 of every 3 deaths in the United States.1 Many forms of CVD have a strong genetic component, and the number of causal genes elucidated greatly depends on the type of CVD under study. Understanding the molecular basis of CVD can guide medical decisions of the practitioner, patient, and family members. Currently, gene panels are used in the clinical setting to screen for disease-causing variation. Panels vary greatly in size depending on the number of genetic causes of the disease in question. There is often a great deal of phenotypic overlap between the inherited CVDs. To accommodate these variable phenotypes, many testing laboratories now use large panels that include >75 genes. Another route that is more commonly being used is the use of whole exome sequencing and whole genome sequencing to identify causal and contributory variants.2,3 Whole exome sequencing or whole genome sequencing can begin with focus on rare variants in genes most commonly associated with disease, and, if no attractive candidates are identified, the search is broadened to include genes not currently associated with disease. Such broad searches can lead to the identification of new disease genes; a great benefit in the research context, but a burden in the clinical-setting making disease risk assessment more difficult. It is also an ever-evolving target to determine the amount of validation necessary to deem variants pathogenic and therefore reportable, especially when variants are unique to individuals or to individual families.4 It can be especially troubling when the disease in question involves sudden death where there is significant risk for surviving family members. It is imperative that new, potential CVD genes continue to be validated using a variety of methods, including family studies, animal- and …

Biological Feature Selection and Disease Gene Identification using New Stepwise Random Forests

Biological Feature Selection and Disease Gene Identification using New Stepwise Random Forests